[unreadable] This project bridges the understanding between physical and chemical principles and genomic/proteomic response by integrating three independent parallel toxicity prediction tools. Each uses computational neural networks (CNNs) and wavelets to rapidly and accurately make pharmaceutical/chemical toxicity predictions. A CNN-based Quantitative Structure-Activity Relationship (QSAR) module makes toxicological predictions based only on structure-activity analyses; a second CNN/wavelet module makes independent toxicogenomic predictions using microarray data; and a third CNN/wavelet module makes toxicogenomic predictions using Massively Parallel Signature Sequencing (MPSS) data. This multi-intelligent, three-module approach provides crosschecks to reduce false positives and false negatives while substantially increasing confidence in predictions relative to current computer-based toxicity prediction techniques. The resulting product could potentially become a primary tool used by (a) human health researchers, b) pharmaceutical companies for screening drugs early during development, c) companies designing/developing new chemicals and chemically treated materials, and (d) government organizations (e.g., military) for mission-related chemical deployments. Public benefits include reduced health and environmental risks (e.g., 4 out of 5 chemicals in use today have inadequate testing); reduced reliance on animal testing; and reduced time and cost required to bring new pharmaceuticals and chemicals into beneficial medical and commercial use. [unreadable] [unreadable] [unreadable]